Sentence Similarity
sentence-transformers
Safetensors
German
English
feature-extraction
Generated from Trainer
dataset_size:16753490
loss:MatryoshkaLoss
loss:MultipleNegativesRankingLoss
Instructions to use MarcGrumpyOlejak/sts-mrl-en-de-base-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use MarcGrumpyOlejak/sts-mrl-en-de-base-v1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("MarcGrumpyOlejak/sts-mrl-en-de-base-v1") sentences = [ "Das ist eine glückliche Person", "Das ist ein glücklicher Hund", "Das ist eine sehr glückliche Person", "Heute ist ein sonniger Tag" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download modules.json from MarcGrumpyOlejak/sts-mrl-en-de-base-v1: direct link, hf CLI and curl.
- Browser
- Download file 117 Bytes
-
https://huggingface.co/MarcGrumpyOlejak/sts-mrl-en-de-base-v1/resolve/main/modules.json
- Command line
-
hf download hf://MarcGrumpyOlejak/sts-mrl-en-de-base-v1/modules.json
-
curl -L -o modules.json https://huggingface.co/MarcGrumpyOlejak/sts-mrl-en-de-base-v1/resolve/main/modules.json
117 Bytes
| [ | |
| { | |
| "idx": 0, | |
| "name": "0", | |
| "path": "", | |
| "type": "sentence_transformers.models.StaticEmbedding" | |
| } | |
| ] |